# alibabacloud-adbpg-mcp-server **Repository Path**: mirrors_aliyun/alibabacloud-adbpg-mcp-server ## Basic Information - **Project Name**: alibabacloud-adbpg-mcp-server - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-18 - **Last Updated**: 2025-11-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # AnalyticDB PostgreSQL MCP Server AnalyticDB PostgreSQL MCP Server serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases. It enables seamless communication between AI Agents and AnalyticDB PostgreSQL, helping AI Agents retrieve database metadata and execute SQL operations. ## Installation You can set up the server either from the source code for development or by installing it from PyPI for direct use. ### Option 1: From Source (for Development) This method is recommended if you want to modify or contribute to the server. ```shell # 1. Clone the repository git clone https://github.com/aliyun/alibabacloud-adbpg-mcp-server.git cd alibabacloud-adbpg-mcp-server # 2. Create and activate a virtual environment using uv uv venv .venv source .venv/bin/activate # On Linux/macOS # .\.venv\Scripts\activate # On Windows # 3. Install the project in editable mode uv pip install -e . ``` ### Option 2: From PyPI (for Production/Usage) This is the simplest way to install the server for direct use within your projects. ```shell pip install adbpg-mcp-server ``` ## Running the Server The server can be run in two transport modes: `stdio` (default) for integration with MCP clients, and `http` for direct API access or debugging. Make sure you have set up the required [Environment Variables](#environment-variables) before running the server. ### Stdio Mode (Default) This is the standard mode for communication with an MCP client. ```bash # Run using the default transport (stdio) uv run adbpg-mcp-server # Or explicitly specify the transport uv run adbpg-mcp-server --transport stdio ``` ### Streamable-HTTP Mode This mode exposes an HTTP server, which is useful for testing, debugging, or direct integration via REST APIs. ```bash # Run the server in HTTP mode on the default host and port (127.0.0.1:3000) uv run adbpg-mcp-server --transport http # Specify a custom host and port uv run adbpg-mcp-server --transport http --host 0.0.0.0 --port 3000 ``` ## MCP Integration To integrate this server with a parent MCP client, add the following configuration to the client's configuration file. The arguments in the `args` array will depend on the transport protocol you choose. ### Example for Stdio Transport ```json "mcpServers": { "adbpg-mcp-server": { "command": "uv", "args": [ "run", "adbpg-mcp-server", "--transport", "stdio" ], "env": { "ADBPG_HOST": "host", "ADBPG_PORT": "port", "ADBPG_USER": "username", "ADBPG_PASSWORD": "password", "ADBPG_DATABASE": "database", "GRAPHRAG_API_KEY": "graphrag llm api key", "GRAPHRAG_BASE_URL": "graphrag llm base url", "GRAPHRAG_LLM_MODEL": "graphrag llm model name", "GRAPHRAG_EMBEDDING_MODEL": "graphrag embedding model name", "GRAPHRAG_EMBEDDING_API_KEY": "graphrag embedding api key", "GRAPHRAG_EMBEDDING_BASE_URL": "graphrag embedding url", "LLMEMORY_API_KEY": "llm memory api_key", "LLMEMORY_BASE_URL": "llm memory base_url", "LLMEMORY_LLM_MODEL": "llm memory model name", "LLMEMORY_EMBEDDING_MODEL": "llm memory embedding model name", "LLMEMORY_ENABLE_GRAPH": "enable graph engine for llm memory (Default: false)" } } } ``` > **Note:** Since `stdio` is the default, you can optionally omit `"--transport", "stdio"` from the `args` array. ### Example for Streamable-HTTP Transport ```json "mcpServers": { "adbpg-mcp-server": { "command": "uv", "args": [ "run", "adbpg-mcp-server", "--transport", "http", "--port", "3000" ], "env": { "ADBPG_HOST": "host", "ADBPG_PORT": "port", "ADBPG_USER": "username", "ADBPG_PASSWORD": "password", "ADBPG_DATABASE": "database", "GRAPHRAG_API_KEY": "graphrag llm api key", "GRAPHRAG_BASE_URL": "graphrag llm base url", "GRAPHRAG_LLM_MODEL": "graphrag llm model name", "GRAPHRAG_EMBEDDING_MODEL": "graphrag embedding model name", "GRAPHRAG_EMBEDDING_API_KEY": "graphrag embedding api key", "GRAPHRAG_EMBEDDING_BASE_URL": "graphrag embedding url", "LLMEMORY_API_KEY": "llm memory api_key", "LLMEMORY_BASE_URL": "llm memory base_url", "LLMEMORY_LLM_MODEL": "llm memory model name", "LLMEMORY_EMBEDDING_MODEL": "llm memory embedding model name", "LLMEMORY_ENABLE_GRAPH": "enable graph engine for llm memory (Default: false)" } } } ``` ### Tools * `execute_select_sql`: Execute SELECT SQL queries on the AnalyticDB PostgreSQL server * `execute_dml_sql`: Execute DML (INSERT, UPDATE, DELETE) SQL queries on the AnalyticDB PostgreSQL server * `execute_ddl_sql`: Execute DDL (CREATE, ALTER, DROP) SQL queries on the AnalyticDB PostgreSQL server * `analyze_table`: Collect table statistics * `explain_query`: Get query execution plan * `adbpg_graphrag_upload` - **Description:** Upload a text file (with its name) and file content to graphrag to generate a knowledge graph. - **Parameters:** - `filename` (`text`): The name of the file to be uploaded. - `context` (`text`): The textual content of the file. * `adbpg_graphrag_query` - **Description:** Query the graphrag using the specified query string and mode。 - **Parameters:** - `query_str` (`text`): the query content. - `query_mode` (`text`): The query mode, choose from `[bypass, naive, local, global, hybrid, mix]`. If null, defaults to `mix`. * `adbpg_graphrag.upload_decision_tree(context text, root_node text)` - **Description:** Upload a decision tree with the specified `root_node`. If the `root_node` does not exist, a new decision tree will be created. - **Parameters:** - `context` (`text`): The textual representation of the decision tree. - `root_node` (`text`): The content of the root node. * `adbpg_graphrag.append_decision_tree(context text, root_node_id text)` - **Description:** Append a subtree to an existing decision tree at the node specified by `root_node_id`. - **Parameters:** - `context` (`text`): The textual representation of the subtree. - `root_node_id` (`text`): The ID of the node to which the subtree will be appended. * `adbpg_graphrag.delete_decision_tree(root_node_entity text)` - **Description:** Delete a sub-decision tree under the node specified by `root_node_entity`. - **Parameters:** - `root_node_entity` (`text`): The ID of the root node of the sub-decision tree to be deleted. * `adbpg_llm_memory_add` - **Description:** Add LLM long memory. - **Parameters:** - `messages` (`json`): The name of the file to be uploaded. - `user_id` (`text`): The user id. - `run_id` (`text`): The run id. - `agent_id` (`text`): The agent id. - `metadata` (`json`): The metadata json(optional). - `memory_type` (`text`): The memory type(optional). - `prompt` (`text`): The prompt(optional). **Note:** At least one of `user_id`, `run_id`, or `agent_id` should be provided. * `adbpg_llm_memory_get_all` - **Description:** Retrieves all memory records associated with a specific user, run or agent. - **Parameters:** - `user_id` (`text`): User ID (optional). If provided, fetch all memories for this user. - `run_id` (`text`): Run ID (optional). - `agent_id` (`text`): Agent ID (optional). If provided, fetch all memories for this agent. **Note:** At least one of `user_id`, `run_id`, or `agent_id` should be provided. * `adbpg_llm_memory_search` - **Description:** Retrieves memories relevant to the given query for a specific user, run, or agent. - **Parameters:** - `query` (`text`): The search query string. - `user_id` (`text`): User ID (optional). If provided, fetch all memories for this user. - `run_id` (`text`): Run ID (optional). - `agent_id` (`text`): Agent ID (optional). If provided, fetch all memories for this agent. - `filter` (`json`): Additional filter conditions in JSON format (optional). **Note:** At least one of `user_id`, `run_id`, or `agent_id` should be provided. * `adbpg_llm_memory_delete_all`: - **Description:** Delete all memory records associated with a specific user, run or agent. - **Parameters:** - `user_id` (`text`): User ID (optional). If provided, fetch all memories for this user. - `run_id` (`text`): Run ID (optional). - `agent_id` (`text`): Agent ID (optional). If provided, fetch all memories for this agent. **Note:** At least one of `user_id`, `run_id`, or `agent_id` should be provided. ### Resources #### Built-in Resources * `adbpg:///schemas`: Get all schemas in the database #### Resource Templates * `adbpg:///{schema}/tables`: List all tables in a specific schema * `adbpg:///{schema}/{table}/ddl`: Get table DDL * `adbpg:///{schema}/{table}/statistics`: Show table statistics ## Environment Variables MCP Server requires the following environment variables to connect to AnalyticDB PostgreSQL instance: - `ADBPG_HOST`: Database host address - `ADBPG_PORT`: Database port - `ADBPG_USER`: Database username - `ADBPG_PASSWORD`: Database password - `ADBPG_DATABASE`: Database name MCP Server requires the following environment variables to initialize graphRAG and llm memory server： - `API_KEY`: API key for LLM provider or embedding API - `BASE_URL`: Base URL for LLM or embedding service endpoint - `LLM_MODEL`: LLM model name or identifier - `EMBEDDING_MODEL`: Embedding model name or identifier ## Dependencies * Python 3.11 or higher * `uv` (for environment and package management)